The evaluation of legal knowledge based systems
ICAIL '99 Proceedings of the 7th international conference on Artificial intelligence and law
The Role of Logic in Computational Models of Legal Argument: A Critical Survey
Computational Logic: Logic Programming and Beyond, Essays in Honour of Robert A. Kowalski, Part II
Induction of defeasible logic theories in the legal domain
ICAIL '03 Proceedings of the 9th international conference on Artificial intelligence and law
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Expert Systems with Applications: An International Journal
Discrimination-aware data mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Rule protection for indirect discrimination prevention in data mining
MDAI'11 Proceedings of the 8th international conference on Modeling decisions for artificial intelligence
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Automatic Decision Support Systems (DSS) are widely adopted for screening purposes in socially sensitive tasks, including access to credit, mortgage, insurance, labor market and other benefits. While less arbitrary decisions can potentially be guaranteed, automatic DSS can still be discriminating in the socially negative sense of resulting in unfair or unequal treatment of people. We present a reference model for finding (prima facie) evidence of discrimination in automatic DSS which is driven by a few key legal concepts. First, frequent classification rules are extracted from the set of decisions taken by the DSS over an input pool dataset. Key legal concepts are then used to drive the analysis of the set of classification rules, with the aim of discovering patterns of discrimination. We present an implementation, called LP2DD, of the overall reference model integrating induction, through data mining classification rule extraction, and deduction, through a computational logic implementation of the analytical tools.